Accuracy of genomic selection: Comparing theory and results
Deterministic predictions of the accuracy of genomic breeding values in selection candidates with no phenotypes have been derived based on the heritability of the trait, number of phenotyped and genotyped animals in the reference population where the marker effects are estimated, the effective population size and the length of the genome. We assessed the value of these deterministic predictions given the results that have been achieved in Holstein and Jersey dairy cattle. We conclude that the deterministic predictions are useful guide for establishing the size of the reference populations which must be assembled in order to predict genomic breeding values at a desired level of accuracy in selection candidates.
A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes
Background: Knowing the phase of marker genotype data can be useful in genome-wide association studies, because it makes it possible to use analysis frameworks that account for identity by descent or parent of origin of alleles and it can lead to a large increase in data quantities via genotype or sequence imputation. Long-range phasing and haplotype library imputation constitute a fast and accurate method to impute phase for SNP data. Methods: A long-range phasing and haplotype library imputation algorithm was developed. It combines information from surrogate parents and long haplotypes to resolve phase in a manner that is not dependent on the family structure of a dataset or on the presence of pedigree information. Results: The algorithm performed well in both simulated and real livestock and human datasets in terms of both phasing accuracy and computation efficiency. The percentage of alleles that could be phased in both simulated and real datasets of varying size generally exceeded 98% while the percentage of alleles incorrectly phased in simulated data was generally less than 0.5%. The accuracy of phasing was affected by dataset size, with lower accuracy for dataset sizes less than 1000, but was not affected by effective population size, family data structure, presence or absence of pedigree information, and SNP density. The method was computationally fast. In comparison to a commonly used statistical method (fastPHASE), the current method made about 8% less phasing mistakes and ran about 26 times faster for a small dataset. For larger datasets, the differences in computational time are expected to be even greater. A computer program implementing these methods has been made available. Conclusions: The algorithm and software developed in this study make feasible the routine phasing of high-density SNP chips in large datasets.
Beef cattle genetic evaluation in the genomics era
Genomic selection is rapidly changing dairy breeding but to date it has had little impact on beef cattle breeding. The challenge for beef is to increase the accuracy of genomic predictions, particularly for those traits that cannot be measured on young animals. Accuracies of genomic predictions in beef cattle are low, primarily due to the relatively low number of animals with genotypes and phenotypes that have been used in gene discovery. To improve this will require the collection of genotypes and phenotypes on many more animals. Several key industry initiatives have commenced in Australia aimed at addressing this issue. Also, unlike dairy, the beef industry includes several major breeds and this will likely require the use of very dense SNP chips to enable accurate genomic prediction equations that are predictive across breeds. In Australia genotyping has been performed on all major breeds and research is underway to ascertain the effectiveness of a high density SNP chip (800K) to increase the accuracy of prediction. However, at this stage it is apparent, even in dairy breeding, that genomic information is best combined with traditional pedigree and performance data to generate genomically-enhanced EBVs, thus allowing greater rates of genetic gain through increased accuracies and reduced generation intervals. Several methods exist for combining the two sources of data into current genetic evaluation systems; however challenges exist for the beef industry to implement these effectively. Over time, as the accuracy of genomic selection improves for beef cattle breeding, changes are likely to be needed to the structure of the breeding sector to allow effective use of genomic information for the benefit of the industry.
Genetic analysis of docility score of Australian Angus and Limousin cattle
The temperament of cattle is believed to affect the profitability of the herd through impacting production costs, meat quality, reproduction, maternal behaviour and the welfare of the animals and their handlers. As part of the national beef cattle genetic evaluation in Australia by BREEDPLAN, 50 935 Angus and 50 930 Limousin calves were scored by seedstock producers for temperament using docility score. Docility score is a subjective score of the animal's response to being restrained and isolated within a crush, at weaning, and is scored on a scale from 1 to 5 with 1 representing the quiet and 5 the extremely nervous or anxious calves. Genetic parameters for docility score were estimated using a threshold animal model with four thresholds (five categories) from a Bayesian analysis carried out using Gibbs sampling in THRGIBBS1F90 with post-Gibbs analysis in POSTGIBBSF90. The heritability of docility score on the observed scale was 0.21 and 0.39 in Angus and Limousin, respectively. Since the release of the docility breeding value to the Australian Limousin population there has been a favourable trend within the national herd towards more docile cattle. Weak but favourable genetic correlations between docility score and the production traits indicates that docility score is largely independent of these traits and that selection to improve temperament can occur without having an adverse effect on growth, fat, muscle and reproduction.
Development of the beef genomic pipeline for BREEDPLAN single step evaluation
Single step genomic BLUP (SS-GBLUP) for BREEDPLAN beef cattle evaluations is currently being tested for implementation across a number of breeds. A genomic data pipeline has been developed to enable efficient analysis of the industry-recorded SNP genotypes for incorporation in SS-GBLUP analyses. Complex data collection, along with format and/or naming convention inconsistencies challenges efficient data processing. This pipeline includes quality control of variable formatted data, and imputation of genotypes, for building the genomic relationship matrix required for implementation into single step evaluation.
Which Genomic Relationship Matrix?
Genomic information can accurately specify relationships among animals, including between those without known common ancestors. Genetic variances estimated with genomic data relate to unknown, more distant, founder populations than those defined by the pedigree. Starting from different sets of assumptions, the properties of some alternative genomic relationship matrices (G) are explored. Although the assumptions and matrices differ, the resulting sets of estimated breeding values predict the differences between animals identically, despite obtaining different estimates of the additive genetic variance - showing that there are many ways of building G that provide identical results. For some methods integer and logic, rather than floating point, operations will expedite building G many-fold.
Breeding polled cattle in Australia
Economic losses in beef cattle due to bruised meat can be largely attributed to the presence of horns. While dehorning practices can provide some economic improvement, it is more labour intensive and is likely to be subject to renewed animal welfare legislation in the future. Breeding naturally polled animals is the long term alternative to reducing economic loss while maintaining best practice animal welfare. The haplotype Poll test is aimed to estimate the Poll genetics of an animal, given the alleles observed at 10 microsatellites in the vicinity of the Poll locus on chromosome 1. The following provides a summary of the genetics of polled cattle and the test used to estimate Poll probability of beef cattle.
Genome-wide association studies of female reproduction in tropically adapted beef cattle
The genetics of reproduction is poorly understood because the heritabilities of traits currently recorded are low. To elucidate the genetics underlying reproduction in beef cattle, we performed a genome-wide association study using the bovine SNP50 chip in 2 tropically adapted beef cattle breeds, Brahman and Tropical Composite. Here we present the results for 3 female reproduction traits: 1) age at puberty, defined as age in days at first observed corpus luteum (CL) after frequent ovarian ultrasound scans (AGECL); 2) the postpartum anestrous interval, measured as the number of days from calving to first ovulation postpartum (first rebreeding interval, PPAI); and 3) the occurrence of the first postpartum ovulation before weaning in the first rebreeding period (PW), defined from PPAI. In addition, correlated traits such as BW, height, serum IGF1 concentration, condition score, and fatness were also examined. In the Brahman and Tropical Composite cattle, 169 [false positive rate (FPR) = 0.262] and 84 (FPR = 0.581) SNP, respectively, were significant (P < 0.001) for AGECL. In Brahman, 41% of these significant markers mapped to a single chromosomal region on BTA14. In Tropical Composites, 16% of these significant markers were located on BTA5. For PPAI, 66 (FPR = 0.67) and 113 (FPR = 0.432) SNP were significant (P < 0.001) in Brahman and Tropical Composite, respectively, whereas for PW, 68 (FPR = 0.64) and 113 (FPR = 0.432) SNP were significant (P < 0.01). In Tropical Composites, the largest concentration of PPAI markers were located on BTA5 [19% (PPAI) and 23% (PW)], and BTA16 [17% (PPAI) and 18% (PW)]. In Brahman cattle, the largest concentration of markers for postpartum anestrus was located on BTA3 (14% for PPAI and PW) and BTA14 (17% PPAI). Very few of the significant markers for female reproduction traits for the Brahman and Tropical Composite breeds were located in the same chromosomal regions. However, fatness and BW traits as well as serum IGF1 concentration were found to be associated with similar genome regions within and between breeds. Clusters of SNP associated with multiple traits were located on BTA14 in Brahman and BTA5 in Tropical Composites.
"SNP Snappy": A Strategy for Fast Genome-Wide Association Studies Fitting a Full Mixed Model
A strategy to reduce computational demands of genome-wide association studies fitting a mixed model is presented. Improvements are achieved by utilizing a large proportion of calculations that remain constant across the multiple analyses for individual markers involved, with estimates obtained without inverting large matrices.
Study of the optimum haplotype length to build genomic relationship matrices
Background. As genomic data becomes more abundant, genomic prediction is more routinely used to estimate breeding values. In genomic prediction, the relationship matrix (A), which is traditionally used in genetic evaluations is replaced by the genomic relationship matrix (G). This paper considers alternative ways of building relationship matrices either using single markers or haplotypes of different lengths. We compared the prediction accuracies and log-likelihoods when using these alternative relationship matrices and the traditional G matrix, for real and simulated data. Methods. For real data, we built relationship matrices using 50k genotype data for a population of Brahman cattle to analyze three traits: scrotal circumference (SC), age at puberty (AGECL) and weight at first corpus luteum (WTCL). Haplotypes were phased with hsphase and imputed with BEAGLE. The relationship matrices were built using three methods based on haplotypes of different lengths. The log-likelihood was considered to define the optimum haplotype lengths for each trait and each haplotype-based relationship matrix. Results. Based on simulated data, we showed that the inverse of G matrix and the inverse of the haplotype relationship matrices for methods using one-single nucleotide polymorphism (SNP) phased haplotypes provided coefficients of determination (R²) close to 1, although the estimated genetic variances differed across methods. Using real data and multiple SNPs in the haplotype segments to build the relationship matrices provided better results than the G matrix based on one-SNP haplotypes. However, the optimal haplotype length to achieve the highest log-likelihood depended on the method used and the trait. The optimal haplotype length (7 to 8 SNPs) was similar for SC and AGECL. One of the haplotype-based methods achieved the largest increase in log-likelihood for SC, i.e. from -1330 when using G to -1325 when using haplotypes with eight SNPs. Conclusions. Building the relationship matrix by using haplotypes that comprise multiple SNPs will increase the accuracy of estimated breeding values. However, the optimum haplotype length that shows the correct relationship among individuals for each trait can be derived from the data.